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Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance

Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance. Published in: Journal IEEE Transactions on Knowledge and Data Engineering archive Volume 26 Issue 3, March 2014. M1 sakusa. Introduction. Text Mining To discover the novel structures or knowledges

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Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance

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  1. Identifying Features in Opinion Mining via Intrinsic and Extrinsic Domain Relevance Published in: Journal IEEE Transactions on Knowledge and Data Engineering archive Volume 26 Issue 3, March 2014 M1 sakusa

  2. Introduction • Text Mining • To discover the novel structures or knowledges • from a large amount of text data such as unstructured data

  3. Opinion Mining (≒ Sentiment Analysis)

  4. Opinion Mining (≒ Sentiment Analysis) opinion feature (or feature)

  5. Related Work • Opinion feature Extraction • Associate Rule Mining (ARM) approach • Latent Dirichlet allocation (LDA) approach

  6. Related Work • Most Existing Feature Extraction on Opinion Mining Approach • Typically only use the knowledge or patterns mined from a given single review corpus. • Completely ignoring the possible variations present in a different corpus about Culture et al.

  7. Suggestion • Intrinsic and Extrinsic Domain Relevance (IEDR) approach • To identify opinion features from online reviews by exploiting the difference in opinion feature statistics across two corpora. • Domain-specific(dependent) corpus (the given review corpus) • Domain-independent corpus (any other corpus) • Domain relevance (DR)

  8. IEDR approach • Over View

  9. IEDR approach • Candidate Feature Extraction • Dependency grammar • subject-verb (SBV) • verb-object (VOB) • preposition-object (POB)

  10. IEDR approach • Candidate Feature Extraction • Dependency grammar • subject-verb (SBV) • verb-object (VOB) • preposition-object (POB) • NN : noun (noun phrases) • CF : candidate features

  11. IEDR approach • Calculating Domain-Relevance • Dispersion • how significantly a term is mentioned across all documents by measuring the distributional significance of the term across different documents in the entire corpus. • Deviation • how frequently a term is mentioned in a particular document by measuring its distributional significance in the document. • Using TF-IDF term weights

  12. IEDR approach Calculating Domain-Relevance N : a total number of N documents M : a total number of M terms i = 1, …, Mj = 1, …, N

  13. IEDR approach Calculating Domain-Relevance (IDR, EDR)

  14. IEDR approach Calculating Intrinsic-relevance & Extrinsic-relevance

  15. IEDR approach Calculating Intrinsic-relevance & Extrinsic-relevance

  16. Experiments Corpus Description Table 3 product.tech.163.com www.lvping.com/hotels Table 4 www.sogou.com/labs

  17. Experiments Evaluated Methods

  18. Experiments • Results • Precision vs Recall cellphone review hotel review

  19. Experiments • Results • Size of Domain-independent Corpus 12 new corpora the size : 2, 4, 6, 8, 10, 20, 30, 40, 50, 60, 70, 80 (thousands)

  20. Experiments • Results • Choice of Domain-Independent Corpus • Single Topic Domain-Independent Corpus cellphone review hotel review

  21. Experiments • Results • Choice of Domain-Independent Corpus • Top-K Topics Domain-Independent Corpus Cellphone review top-1 : 8000(Culture) top-2 : 4000(Culture) + 4000(Sports) top-3 : 2667(Culture) + 2667(Sports) + 2666(Tourism) Hotel review top-1 : 8000(Culture) top-2 : 4000(Culture) + 4000(Education) top-3 : 2667(Culture) + 2667(Education) + 2666(Health)

  22. Experiments • Results • Domain Relevance Thresholds

  23. Experiments • Results • Feature-Based Opinion Mining Application

  24. Conclusion • We proposed a novel intercorpus statistics approach to opinion feature extraction based on the IEDR feature-filtering criterion. • Experimental results demonstrate that the proposed IEDR not only leads to noticeable improvement over either IDR or EDR, but also outperforms four main- stream methods. • For future work, we will employ fine-grained topic modeling approach to jointly identify opinion features, including non-noun features, infrequent features, as well as implicit features. We plan to further test the IEDR opinion feature extraction in several • We have had some preliminary success in applying IEDR to extract English opinion features from hotel reviews.

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